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Queue wait-time estimator

Work out how long the next person will wait at your clinic, counter or service desk, and whether the queue is growing or stable. Useful for deciding when to add a counter, and for seeing why a WhatsApp token system pays for itself.

Expected wait for the next person

38 min

Capacity24 per hour
Utilisation167%
Queue growth+16 per hour

Above 90% utilisation, waits balloon with any hiccup. Add a counter at peak hours, or let people wait outside with a WhatsApp token.

How to use it

  1. Enter how many people arrive per hour at your busiest time.

  2. Enter how many each counter or doctor can serve per hour, and how many are working.

  3. Set how many are already waiting. The expected wait, utilisation and queue growth update live.

Why queues explode above 90%

When arrivals get close to capacity, every small delay, a long consultation or a late doctor, has nowhere to go. Waits rise steeply, not gradually. Keeping peak utilisation around 80% is the difference between a 15-minute and a 90-minute wait.

Two ways to fix a long wait

Add capacity at peak (a second counter from 9 to 11) or spread arrivals (appointments, or tokens that let people arrive when their turn is near). The second is cheaper and is exactly what a WhatsApp token system does: people wait at home and walk in when messaged.

What the numbers mean for a clinic

An OPD seeing 40 walk-ins an hour with two doctors at 12 each is at 167% utilisation: the queue grows by 16 people every hour and the last patient of the morning waits over two hours. A third doctor or staggered tokens brings it under control.

Common questions

How accurate is this?

It is a simple capacity model, good for planning. Real queues vary with no-shows and consultation length.

What is a good utilisation?

Around 80% at peak. Above 90% waits become unpredictable.

How does a WhatsApp token help?

People get a number by message and are told when to come, so the waiting area empties and arrivals smooth out. See Token Management.

Can it model multiple departments?

Run it once per department. The full product tracks each queue live.

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